Proceedings - Österreichische Gesellschaft für Artificial Intelligence

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Anja Stukenbrock, and Susanne Uhmann, 2009. Gesprächsanalytisches Transkriptionssystem 2 (GAT 2). Gesprächsforschung - Online-Zeitschrift zur verbalen Interaktion, 10 edition. Nick Webb, David Benyon, Jay Bradley, Preben Hansen, and Oli Mival. 2010. Wizard of oz experiments for a companion dialogue system: Eliciting companionable conversation. In Proceedings of the Seventh conference on International Language Resources and Evaluation (LREC’10). European Language Resources Association (ELRA). Y. Wilks. 2010. Close Engagements with Artificial Companions: Key Social, Psychological and Design issues. John Benjamins, Amsterdam. Markus Wolf, Andrea B. Horn, Matthias R. Mehl, Severin Haug, James W. Pennebraker, and Hans Kordy. 2008. Computergestützte quantitative Textanalyse. In Diagnostica, volume Vol. 54, Number 2/2008. Hogrefe, Göttingen. 154 Proceedings of KONVENS 2012 (Main track: oral presentations), Vienna, September 20, 2012
S-restricted monotone alignments Steffen Eger Faculty of Economics Goethe Universität Frankfurt Grüneburg-Platz 1, 60323 Frankfurt am Main, Germany steffen.eger@yahoo.com Abstract We present an alignment algorithm for monotone many-to-many alignments, which are relevant e.g. in the field of grapheme-to-phoneme conversion (G2P). Moreover, we specify the size of the search space for monotone many-to-many alignments in G2P, which indicates that exhaustive enumeration is generally possible, so that some limitations of our approach can easily be overcome. Finally, we present a decoding scheme, within the monotone many-to-many alignment paradigm, that relates the decoding problem to restricted integer compositions and that is, putatively, superior to alternatives suggested in the literature. 1 Introduction Grapheme-to-phoneme conversion (G2P) is the problem of transducing, or converting, a grapheme, or letter, string x over an alphabet Σ x into a phoneme string y over an alphabet Σ y . An important first step thereby is finding alignments between grapheme and phoneme strings in training data. The classical alignment paradigm has presupposed alignments that were (i) one-to-one or one-to-zero; i.e. one grapheme character is mapped to at most one phoneme character; this assumption has probably been a relic of both the traditional assumptions in machine translation (Brown et al. 1990) and in biological sequence alignment (Needleman and Wunsch, 1970). In the field of G2P such alignment models are sometimes also called ɛ-scattering models (Black et al., 1998). (ii) monotone, that is, the order between characters in grapheme and phoneme strings is preserved. It is clear that, despite its benefits, the classical alignment paradigm has a couple of limitations; in particular, it may be unable to explain certain grapheme-phoneme sequence pairs, a.o. those where the length of the phoneme string is greater than the length of the grapheme string such as in exact igzækt where x has length 5 and y has length 6. In the same context, even if an input pair can be explained, the one-to-one or one-to-zero assumption may lead to alignments that, linguistically, seem nonsensical, such as p h o e n i x f – i: n i k s where the reader may verify that, no matter where the ɛ is inserted, some associations will always appear unmotivated. Moreover, monotonicity appears in some cases violated as well, such as in the following, centre sent@r where it seems, linguistically, that the letter character r corresponds to phonemic r and graphemic word final e corresponds to @. 155 Proceedings of KONVENS 2012 (Main track: oral presentations), Vienna, September 20, 2012
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11 th Conference on Natural Languag
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Table of Contents Main conference P
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Comparing variety corpora with vis-
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LexSem 2012: Workshop on Recent Dev
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KONVENS 2012 The 11 th Conference o
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Organizers: Jeremy Jancsary (ÖGAI/
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Ines Rehbein (Potsdam University) M
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Integrating viewpoints into newspap
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Phonetically aided syntactic parsin
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The Statistical Approach to Natural
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Data-driven Knowledge Extraction fo
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4 The Different Relations Types In
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Relation Type #Patterns Examples Su
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Suits-to Served-with Substituted-by
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References Timothy Chklovski and Pa
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1.2 Problem Definitions In a MRA, t
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oot person organisation product ...
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Group Organis. Persons Products all
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Method α α+β α+γ all SentiWS 0
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A Supervised POS Tagger for Written
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Our tagset mixes and matches tags a
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ecause the enclitic pronoun is an i
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stochastic rules as opposed to larg
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Kevin Duh, and Katrin Kirchhoff. 20
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forms, and that existentials can be
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Pseudo Verb Punctuation PNC
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NLP Workflow for On-line Definition
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nological phrases on the basis of p
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Figure 2: A screenshot of the entir
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When analyzing the evaluation sets
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Projecting Semantic Roles via Tai M
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quent prediction, return it (iii) i
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for the B/T/FT settings. This is pr
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ut it is implied by Table 3 and Fig
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References Wanxiang Che, Zhenghua L
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Our ultimate goal is to develop com
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ADORNING CAUSE_FLUIDIC_MOTION COTHE
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English nouns, verbs, adjectives, a
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considerably worse than FrameNet. T
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Extraction. In Proceedings of the 1
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lexicon from a document-derived voc
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Initialize hypernym set H to contai
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ulary distance. Let d(a, b) denote
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the reduced lexicon (243) was neare
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Adding nominal spice to SALSA - fra
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SALSA verbs nouns 0 FE 0 2 1 FE 68
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gen, which in one of its meanings i
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In the next section we describe how
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Acknowledgments This work was funde
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most commonly used (Turney and Pant
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anteed because the forward feature
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Page 111 and 112: those candidate phrases that are an
Page 113 and 114: 5.2 Evaluation Metrics for WPD The
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Page 117 and 118: Evaluations (SemEval-2007), June (2
Page 119 and 120: 2 Related Work The majority of pape
Page 121 and 122: and Roth, 2009; Lin and Wu, 2009).
Page 123 and 124: Features Dev Test CoNLL-2003 w 0 +
Page 125 and 126: Feature Dev Test Test+ — 93.78 91
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Page 129 and 130: tion. It was first included as a sp
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Page 135 and 136: Información, (JOTRI 2003), pages 1
Page 137 and 138: Phonetically aided Syntactic Parsin
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Page 145 and 146: Linguistic analyses of the LAST MIN
Page 147 and 148: Table 1: Comparison between corpora
Page 149 and 150: Table 2: Most frequent POS patterns
Page 151 and 152: Table 3: A problematic dialog situa
Page 153: 6 Summary We have presented the cur
Page 157 and 158: s k n i i: f phh ohehnh ih xh s k n
Page 159 and 160: Dataset µ G µ P P [G>15] P [P >15
Page 161 and 162: Perplexity H(L | P ) 2PKE-Uni 7.002
Page 163 and 164: (1, 1) (2, 1) (3, 1) (4, 1) (1, 2)
Page 165 and 166: Use of linguistic features for impr
Page 167 and 168: whereas English has a verb-initial
Page 169 and 170: Accuracy of Accuracy of Accuracy of
Page 171 and 172: English Persian Train: Sentences 30
Page 173 and 174: to compensate for sparsity of paral
Page 175 and 176: Name # Documents # Tokens # Lemmas
Page 177 and 178: Figure 1: Precision-Recall graphs c
Page 179 and 180: A Multi-level Annotation Model for
Page 181 and 182: and sentence level with the aim of
Page 183 and 184: (Schmid, 1995; Schiller et al., 199
Page 185 and 186: EI EII Tag AKR MARK Typography ITER
Page 187 and 188: References Saima Aman and Stan Szpa
Page 189 and 190: Robust processing of noisy web-coll
Page 191 and 192: 1. first strip of the papery skin o
Page 193 and 194: References Jon Chamberlain, Massimo
Page 195 and 196: ments between its LSRs. Additionall
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Page 199 and 200: Using Information Retrieval Technol
Page 201 and 202: against every tree. Others avoid th
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# #Documents Index Size 1 29,704 1.
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References P. Bański, C. Belica, H
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(2012), in their corpus study of va
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DET NSUBJ ADV OBJA AMOD Seine Tante
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A Computational Semantic Analysis o
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(6) a. Is Spain very large? b. Is B
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References Alan C. Bale. 2008. A Un
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219 Proceedings of KONVENS 2012 (Ma
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221 Proceedings of KONVENS 2012 (Ma
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Using subcategorization frames to i
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5.2 Word clustering based on the Le
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erage of english grammars. In Proce
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cases not disambiguated using langu
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Not surprisingly, the results show
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Automatic Identification of Languag
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Differences also appear at the synt
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Acknowledgments The authors express
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2 Related Work Previous work on POS
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The XY tag is used for all non-word
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Comparing Variety Corpora with Vis-
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INPUT variety corpus comparability
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References Andrea Abel and Stefanie
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Features Context features (token wi
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0.7 0.7 0.6 0.6 0.5 0.5 F-measure 0
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ties into fine-grained classes. In
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verbials (e.g., therefore, still).
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word raw freq. non conn. conn. (of
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Corpus-based Acquisition of German
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Events Objects IPrec CPrec IPrec CP
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References Marco Baroni, Silvia Ber
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Preface The present proceedings con
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Workshop Program Friday, September
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Unsupervised Sentiment Analysis wit
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pling (Geman and Geman, 1984) to es
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References Anders Björkelund, Bern
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Class # Events in % neutral 45’01
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3.1.1 Rules Let t 1..n be tokens of
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Human 6 System 6 Human 4 System 4 A
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Codebuch Akteursanalyse. rev. 2011.
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of our work in the whole applicatio
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3.4 Negation and graduation Valence
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local context. For instance, in une
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n’était pas très favorable au G
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Domain-specific variation of sentim
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query building blocks comments real
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References Monica Bednarek. 2006. E
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3 Basic Methodology of Annotation T
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(3) A. V případě jeho kandidatur
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4.3 Auxiliary data - Mall.cz We hav
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Overlap f-score Source 0.750 Polar
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A Phrase-Based Opinion List for the
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the Stuttgart-Tübingen Tagset (STT
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As for noun-based opinion phrases a
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More than half of the words and phr
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treebank. In Proceedings of the Wor
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Apart from these, we distinguish op
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close variants)”. In fact importa
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What is the Meaning of 5 *’s? An
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For each category, we follow (Const
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Positivity Term 2.87692577130 elske
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In recent years, the use of rating
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KONVENS 2012 LThist 2012: First Int
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Organisation Committee: Thierry Dec
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Rule-Based Normalisation of Histori
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isation rules were applied to the i
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ments in the gold standard have bee
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cation in the contemporary SUC samp
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proach. References Maria Ågren, Ro
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2 Related Work There has been consi
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vided to ensure that they are norma
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alects, or even languages. Norma ca
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ization (rather than modernization)
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The Sketch Engine as infrastructure
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2.1 The Corpus of English Dialogues
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Other notable contrasts are that 17
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Evaluating a Post-editing Approach
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Figure 1: Example of a marriage lic
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all possible concatenations of indi
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Table 3: Post-editing results. Time
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okstaffua, bokstaffwa, bokstafwa, b
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Rule Cost v↔u 0.1 v↔w 0.1 u↔o
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graphic word boundaries and conside
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ation. Gulordava and Baroni (2011),
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ut adding a statistical measure to
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e.g. philosophical and political pr
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of the EACL 2009 Workshop on GEMS:
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Most of the research on genre class
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Precision Recall F 1 Unigrams 0.551
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The DTA ‘base format’: A TEI-Su
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Lib are complementary in the sense
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inline metadata solutions for edito
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ing. The fourth level contains an e
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the work package ‘Resources and T
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is mainly used with parallel corpor
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Corrections by the editor were omit
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Accuracy N % All Words 781/1000 78.
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$$ $$ $$ $$ $$ $$ $$ $$ $$ Figure 4
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Digitised Historical Text: Does it
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3 Related Work Previous research on
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5.1 End-of-line Soft Hyphen Deletio
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quite difficult to get an idea of h
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References Ahmad Abdulkader and Mat
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cision, recall, and F1. Section 4 d
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not corrected corrected p P R F1 P
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From Semi-Automatic to Automatic Af
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Figure 2: ER-Diagramm of the Morphi
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Figure 4: Morphilizer’s implement
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Figure 6: Architecture of Morphorm
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References Harald R. Baayen, R. Pie
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edition has been adopted. This is d
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marginalia, emendations or punctuat
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Word Lemma Class Subclass Type Tens
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Figure 3. Sample screenshot of comp
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KONVENS 2012 LexSem 2012: Workshop
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English) closely and discusses the
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verbs were collected mostly from an
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3 Theoretical Background Let us now
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are sensible to semantic properties
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Cançado, Márcia and Amaral, Luana
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morphological tagger for Russian la
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whom/which something is taken. (7cz
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Verb class same frame different fra
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Construction Grammar are introduced
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posed to Hon åker på måndag ‘S
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nitions of Agent, for example. Inst
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Framenet++, VR dnr 2010-6013), by t
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Källström and Inger Lindberg, edi
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scoring function σ : W ×L×W →
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esults in the triple 〈heutige Son
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Unlexicalized links 7,833,635 n (i)
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The difference in accuracies betwee
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Towards high-accuracy bilingual phr
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over a set of candidate translation
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each soft restriction soft m set on
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(> 98% in scalable precision) and a
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References Colin Bannard and Chris
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Preface The need for automatically
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15:50-16:15 Coffee break 16:15-16:4
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Standards for the technical infrast
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⎡ ⎤ phrase CATEGORY np ⎡ ⎤
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The representation of the feature v
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ement, while the interpretation as
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hand, checking the validity of FSR
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Standardizing metadata descriptions
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2 LMF and semantic interoperability
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can register and create DCs. Users
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all kinds of lexemes, (see Cruse (1
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constraints, because many aspects o
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Menzo Windhouwer. 2012. RELcat: a R
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the meeting in Rome as a starting p
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As these documents provide the refe
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Two multi-layer annotated corpora r
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As audio data is not directly store
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Jörg Mayer. 1995. Transcription of
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Towards standardized lexical semant
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Getting involved into language reso
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